Beginning Python - From Novice to Professional

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CHAPTER 17 ■ ■ ■ Extending Python You can implement anything in Python, really; it’s a powerful language, but sometimes it can get a bit . . . too slow. For example, if you’re writing a scientific simulation of some form of nuclear reaction, or you’re rendering the graphics for the next Star Wars movie (wait—there won’t be any more now, will there?), writing the high-performance code in Python will probably not be a good choice. Python is meant to be easy to work with, and to help make the development fast. The flexibility needed for this comes with a hefty price in terms of efficiency. It’s certainly fast enough for most common programming tasks, but if you need real speed, languages such as C, C++, and Java can usually beat it by several orders of magnitude. Now, I don’t want to encourage the speed freaks among you to start developing exclusively in C; remember, this may speed up the program itself, but it will most certainly slow down your programming. So you need to consider what is most important: getting the program done quickly, or eventually (in the distant future) getting a program that runs really, really fast. If Python is fast enough, the extra pain involved will make using a low-level language such as C something of a meaningless choice (unless you have other requirements, such as running on an embedded device that doesn’t have room for Python, or something like that). This chapter deals with the cases where you do need extra speed. The best solution then probably isn’t to switch entirely to C (or some other low- or mid-level language); instead, I recommend the following approach, which has worked for plenty of industrial-strength speed freaks out there (in one form or another): 1. Develop a prototype in Python. (See Chapter 19 for some material on prototyping.) 2. Profile your program and determine the bottlenecks. (See Chapter 16 for some material on testing.) 3. Rewrite the bottlenecks as a C (or C++, C#, Java, Fortran, etc.) extension. The resulting architecture—a Python framework with one or more C components—is a very powerful one, because it combines the best of two worlds. It’s a matter of choosing the right tools for each job. It affords you the benefits of developing a complex system in a high-level language (Python), and it lets you develop your smaller (and presumably simpler) speed-critical components in a low-level language (C). If you have some knowledge of what the bottlenecks of your system will be even before you begin, you can (and probably should) design your prototype so that replacing the critical parts is easy. I think I might as well state this in the form of a tip: 357
358 CHAPTER 17 ■ EXTENDING PYTHON ■Tip Encapsulate potential bottlenecks. You may find that you don’t need to replace the bottlenecks with C extensions (perhaps you suddenly got ahold of a faster computer), but at least the option is there. There is another situation that is a common use case for extensions as well: legacy code. There may be some existing code that you want to use, and it exists only in, say, C. You can then “wrap” this code (write a small C library that gives you a proper interface) and create a Python extension library from your wrapper. In the following sections, I give you some starting points for extending both the classical C implementation of Python, either by writing all the code yourself or by using a tool called SWIG, and for extending two other implementations: Jython and IronPython. You will also find some hints about other options for accessing external code. Read on . . . WHAT ABOUT FORTRAN? While I won’t explicitly discuss how to connect Python with Fortran in this chapter, it is certainly possible. Fortran was the first “real” programming language (originally developed in 1954). Still, in some areas, Fortran is the language of choice for high-performance computing. If you want to (or, perhaps more likely, have to) use Fortran for your extensions, you should check out Pyfort (http://pyfortran.sf.net) and F2PY (http:// cens.ioc.ee/projects/f2py2e). The Really Easy Way: Jython and IronPython If you happen to be running Jython or IronPython (both mentioned in Chapter 1), extending your Python with native modules is quite easy. The reason for this is that Jython and IronPython give you direct access to modules and classes from the underlying languages (Java for Jython and C# and other .NET languages for IronPython), so you don’t have to conform to some specific API (as you have to when extending CPython)—you simply implement the functionality you need, and, as if by magic, it will work in Python. As a case in point, you can access the Java standard libraries directly in Jython and the C# standard libraries directly in IronPython. Listing 17-1 shows a simple Java class. You can compile this with some Java compiler, such as javac (freely downloadable from http://java.sun.com). Listing 17-1. A Simple Java Class (JythonTest.java) public class JythonTest { } public void greeting() { System.out.println("Hello, world!"); }
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Beginning Python From Novice to Pro
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Index ■Symbols - operator 558 !=
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